Leveraging Semantic Web technologies for more relevant E-tourism Behavioral Retargeting

The e-tourism is today an important field of the e-commerce. One specificity of this field is that consumers spend much time comparing many options on multiple websites before purchasing. It's easy for consumers to forget the viewed offers or websites. The Behavioral Retargeting (BR) is a widely used technique for online advertising. It leverages consumers' actions on advertisers' websites and displays relevant ads on publishers' websites. In this paper, we're interested in the relevance of the displayed ads in the e-tourism field. We present MERLOT 1, a Semantic-based travel destination recommender system that can be deployed to improve the relevance of BR in the e-tourism field. We conducted a preliminary experiment with the real data of a French travel agency. The results of 33 participants showed very promising results with regards to the baseline according to all used metrics. By this paper, we wish to provide a novel viewpoint to address the BR relevance problem, different from the dominating machine learning approaches.